Extractive method for speaker identification in texts with self-training

ABSTRACT

A method, computer program, and computer system is provided for identifying a speaker in at text based work. Labeled and unlabeled instances corresponding to one or more speakers are extracted. Pseudo-labels are inferred for the extracted unlabeled instances based on the labeled instances. One or more of the unlabeled instances are labeled based on the inferred pseudo-labels.

FIELD

This disclosure relates generally to field of computing, and moreparticularly to natural language processing.

BACKGROUND

Speaker identification in texts aims to identify the speaker(s) for eachutterance in texts such as books. Each utterance may correspond to asingle speaker, multiple speakers, or an unnamed speaker (e.g., a nounphrase). This task may be divided into several subtasks, such asquotation identification, named entity recognition, coreferenceresolution, candidate speaker identification, and feature-basedclassification.

SUMMARY

Embodiments relate to a method, system, and computer readable medium foridentifying a speaker in a text-based work. According to one aspect, amethod for identifying a speaker in a text-based work is provided. Themethod may include extracting labeled and unlabeled instancescorresponding to one or more speakers. Pseudo-labels are inferred forthe extracted unlabeled instances based on the labeled instances. One ormore of the unlabeled instances are labeled based on the inferredpseudo-labels.

According to another aspect, a computer system for identifying a speakerin a text-based work is provided. The computer system may include one ormore processors, one or more computer-readable memories, one or morecomputer-readable tangible storage devices, and program instructionsstored on at least one of the one or more storage devices for executionby at least one of the one or more processors via at least one of theone or more memories, whereby the computer system is capable ofperforming a method. The method may include extracting labeled andunlabeled instances corresponding to one or more speakers. Pseudo-labelsare inferred for the extracted unlabeled instances based on the labeledinstances. One or more of the unlabeled instances are labeled based onthe inferred pseudo-labels.

According to yet another aspect, a computer readable medium foridentifying a speaker in a text-based work is provided. The computerreadable medium may include one or more computer-readable storagedevices and program instructions stored on at least one of the one ormore tangible storage devices, the program instructions executable by aprocessor. The program instructions are executable by a processor forperforming a method that may accordingly include extracting labeled andunlabeled instances corresponding to one or more speakers. Pseudo-labelsare inferred for the extracted unlabeled instances based on the labeledinstances. One or more of the unlabeled instances are labeled based onthe inferred pseudo-labels.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other objects, features and advantages will become apparentfrom the following detailed description of illustrative embodiments,which is to be read in connection with the accompanying drawings. Thevarious features of the drawings are not to scale as the illustrationsare for clarity in facilitating the understanding of one skilled in theart in conjunction with the detailed description. In the drawings:

FIG. 1 illustrates a networked computer environment according to atleast one embodiment;

FIG. 2A is a framework for an extractive speaker identification model,according to at least one embodiment;

FIG. 2B is a self-training framework for training the extractive speakeridentification model, according to at least one embodiment;

FIG. 3 is an operational flowchart illustrating the steps carried out bya program for identifying a speaker in a text-based work, according toat least one embodiment;

FIG. 4 is a block diagram of internal and external components ofcomputers and servers depicted in FIG. 1 according to at least oneembodiment;

FIG. 5 is a block diagram of an illustrative cloud computing environmentincluding the computer system depicted in FIG. 1 , according to at leastone embodiment; and

FIG. 6 is a block diagram of functional layers of the illustrative cloudcomputing environment of FIG. 5 , according to at least one embodiment.

DETAILED DESCRIPTION

Detailed embodiments of the claimed structures and methods are disclosedherein; however, it can be understood that the disclosed embodiments aremerely illustrative of the claimed structures and methods that may beembodied in various forms. Those structures and methods may, however, beembodied in many different forms and should not be construed as limitedto the exemplary embodiments set forth herein. Rather, these exemplaryembodiments are provided so that this disclosure will be thorough andcomplete and will fully convey the scope to those skilled in the art. Inthe description, details of well-known features and techniques may beomitted to avoid unnecessarily obscuring the presented embodiments.

Embodiments relate generally to the field of computing, and moreparticularly to natural language processing. The following describedexemplary embodiments provide a system, method and computer program to,among other things, identify a speaker in a text-based work. Therefore,some embodiments have the capacity to improve the field of computing byallowing for a computer to determine a speaker based on the content of atextual work based on developing likely pseudo-labels from a corpus ofknown and unknown works.

As previously described, speaker identification in texts aims toidentify the speaker(s) for each utterance in texts such as books. Eachutterance may correspond to a single speaker, multiple speakers, or anunnamed speaker (e.g., a noun phrase). This task may be divided intoseveral subtasks, such as quotation identification, named entityrecognition, coreference resolution, candidate speaker identification,and feature-based classification.

However, each module is imperfect itself, and the inevitably propagatederrors can seriously affect the final performance. This task may beconsidered as a span extraction task and formulate it as a standardmachine reading comprehension (MRC) problem to leverage more contextinformation. By reformulating this task, the method does not rely on anypre-trained models of other tasks (e.g., named entity recognition andcoreference resolution) nor a pre-defined character list yet achievestate-of-the-art performance on all public speaker identificationdatasets for Chinese. Furthermore, the method can be easily adapted toother languages without requiring transferring dozens of features thatare carefully created or selected by humans for a single language.

Additionally, this pipeline has several limitations. First, each moduleis imperfect itself, and the inevitably propagated errors can seriouslyaffect the final performance. For example, the performance of thestate-of-the-art coreference resolution model is about 80.3% in F1.Second, this classical pipeline assumes that a speaker can only be asingle entity, which is not always the case as it happens when thereexist multiple speakers at the same time as well as unnamed speakers(e.g., “a young girl”). Third, features are usually carefully createdand selected by humans, which may make these features hard to be usedfor other languages. For example, previous studies design a variety oflanguage-specific features such as distance to utterance, gendermatching, speaker name in utterance, speech verbs, etc. Finally,previous speaker identification methods heavily rely on human-annotateddata, which is usually small-scale considering the expensive andtime-consuming human annotation process and may constrain theperformance of pre-trained language models (such as BERT) with millionsof parameters on small-scale speaker identification datasets.

It may be advantageous, therefore, to convert speaker identification toan extractive machine reading comprehension task, which aims to extracta span from a given document to answer a given question. Byreformulating the task in this way, intermediate steps, such as namedentity recognition and coreference resolution, may be skipped to avoidintroducing errors. Furthermore, there may not be a need to design anylanguage-specific features, making it easy to adapt this method to otherlanguages. As spans may not be limited to be entities, the model canidentify speakers of different types or forms. To overcome thelimitation caused by small-scale human-annotated data, inspired by theclassical self-training paradigm, a large number of pseudo-label datamay be generated based on large-scale unlabeled books and used asadditional data for training, which may also help advanced pre-trainedlanguage models achieve better results on this task. The extractivemethod does not need a given candidate speaker list yet achieve betterresults than previous state-of-the-art methods on both datasets. Exactmatch (EM) may be used as the evaluation metrics: a model's predictionis correct only if it exactly matches the ground truth answer. Previousmethods use accuracy as the evaluation metric since a candidate list isprovided. As each instance in these datasets must have a ground truthspeaker, here exact match can be regarded as accuracy.

Aspects are described herein with reference to flowchart illustrationsand/or block diagrams of methods, apparatus (systems), and computerreadable media according to the various embodiments. It will beunderstood that each block of the flowchart illustrations and/or blockdiagrams, and combinations of blocks in the flowchart illustrationsand/or block diagrams, can be implemented by computer readable programinstructions.

The following described exemplary embodiments provide a system, methodand computer program that identifies a speaker in a text-based work.Referring now to FIG. 1 , a functional block diagram of a networkedcomputer environment illustrating a speaker identification system 100(hereinafter “system”) for identifying a speaker in a text-based work isdepicted. It should be appreciated that FIG. 1 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

The system 100 may include a computer 102 and a server computer 114. Thecomputer 102 may communicate with the server computer 114 via acommunication network 110 (hereinafter “network”). The computer 102 mayinclude a processor 104 and a software program 108 that is stored on adata storage device 106 and is enabled to interface with a user andcommunicate with the server computer 114. As will be discussed belowwith reference to FIG. 4 the computer 102 may include internalcomponents 800A and external components 900A, respectively, and theserver computer 114 may include internal components 800B and externalcomponents 900B, respectively. The computer 102 may be, for example, amobile device, a telephone, a personal digital assistant, a netbook, alaptop computer, a tablet computer, a desktop computer, or any type ofcomputing devices capable of running a program, accessing a network, andaccessing a database.

The server computer 114 may also operate in a cloud computing servicemodel, such as Software as a Service (SaaS), Platform as a Service(PaaS), or Infrastructure as a Service (laaS), as discussed below withrespect to FIGS. 5 and 6 . The server computer 114 may also be locatedin a cloud computing deployment model, such as a private cloud,community cloud, public cloud, or hybrid cloud.

The server computer 114, which may be used for identifying a speaker ina text-based work is enabled to run a Speaker Identification Program 116(hereinafter “program”) that may interact with a database 112. TheSpeaker Identification Program method is explained in more detail belowwith respect to FIG. 3 . In one embodiment, the computer 102 may operateas an input device including a user interface while the program 116 mayrun primarily on server computer 114. In an alternative embodiment, theprogram 116 may run primarily on one or more computers 102 while theserver computer 114 may be used for processing and storage of data usedby the program 116. It should be noted that the program 116 may be astandalone program or may be integrated into a larger speakeridentification program.

It should be noted, however, that processing for the program 116 may, insome instances be shared amongst the computers 102 and the servercomputers 114 in any ratio. In another embodiment, the program 116 mayoperate on more than one computer, server computer, or some combinationof computers and server computers, for example, a plurality of computers102 communicating across the network 110 with a single server computer114. In another embodiment, for example, the program 116 may operate ona plurality of server computers 114 communicating across the network 110with a plurality of client computers. Alternatively, the program mayoperate on a network server communicating across the network with aserver and a plurality of client computers.

The network 110 may include wired connections, wireless connections,fiber optic connections, or some combination thereof. In general, thenetwork 110 can be any combination of connections and protocols thatwill support communications between the computer 102 and the servercomputer 114. The network 110 may include various types of networks,such as, for example, a local area network (LAN), a wide area network(WAN) such as the Internet, a telecommunication network such as thePublic Switched Telephone Network (PSTN), a wireless network, a publicswitched network, a satellite network, a cellular network (e.g., a fifthgeneration (5G) network, a long-term evolution (LTE) network, a thirdgeneration (3G) network, a code division multiple access (CDMA) network,etc.), a public land mobile network (PLMN), a metropolitan area network(MAN), a private network, an ad hoc network, an intranet, a fiberoptic-based network, or the like, and/or a combination of these or othertypes of networks.

The number and arrangement of devices and networks shown in FIG. 1 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 1 . Furthermore, two or more devices shown in FIG. 1 maybe implemented within a single device, or a single device shown in FIG.1 may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) of system100 may perform one or more functions described as being performed byanother set of devices of system 100.

Referring now to FIG. 2A, a framework for an extractive speakeridentification model 200A is depicted. As instances in all existingspeaker identification datasets provide ground truth segmentedutterances, the provided context that contains an utterance may beregarded as the given document, the utterance itself as a question, andthe ground truth speaker that appears in the document as the extractiveanswer span. For the practical usage when utterance labels areunavailable, the method can be adapted by adding additional traininginstances. For example, given a piece of text that may contain anutterance (i.e., quotation marks are included), the speaker may beannotated if there indeed exists an utterance, otherwise, the content inthe quotation marks as well as marks are labeled as the extractiveanswer span.

The extractive MRC model may be built upon RoBERTa-wwm-ext-large, apre-trained language model for Chinese that is widely used on manynatural language understanding tasks for Chinese. It may be appreciatedthat the disclosed method can be easily used upon other recentlyreleased pre-trained language models.

To construct the input sequence, a special class token (CLS), tokens ina given piece of text q that may contain an utterance or a givenutterance, a special separator token (SEP), and tokens in the given textd that covers the piece of text q may be concatenated. Two vectorsp_(start) and p_(end) are introduced to represent the estimatedprobabilities of each token in d to be the start or end token of theanswer span a that appears in d, respectively. Let a_(start) and a_(end)denote the start offset and end offset of a, respectively.

The extractive MRC model may be optimized with parameters θ byminimizing Σ_(t∈V)L(t, θ), where V represents the set of speakeridentification instances, and L is defined as:

L(t,θ)=−log p _(start,θ)(a _(start) |t)−log p _(end,θ)(a _(end) |t)

Referring now to FIG. 2B, a self-training framework 200B for trainingthe extractive speaker identification model 200A is depicted. To enableend-to-end training, known works may be annotated with a speaker if oneexists. Otherwise, content within quotation marks and other marks may beannotated. Additional works may be annotated as a development set, andfurther instances may be collected as unlabeled works. Unlabeledextractive speaker identification instances may be generated from theunlabeled works at (1). The labeled data may be used to train a teachermodel at (2). The resulting teacher model may be used to inferpseudo-labels for the unlabeled instances. A student model may betrained with the combination of pseudo-labeled and labeled data at (3).The student model may be regarded as a new teacher model, and theprocess may repeat

Referring now to FIG. 3 , an operational flowchart illustrating thesteps of a method 300 carried out by a program that identifies a speakerin a text-based work is depicted.

At 302, the method 300 may include extracting labeled and unlabeledinstances corresponding to one or more speakers.

At 304, the method 300 may include inferring pseudo-labels for theextracted unlabeled instances based on the labeled instances.

At 306, the method 300 may include labeling one or more of the unlabeledinstances based on the inferred pseudo-labels.

It may be appreciated that FIG. 3 provides only an illustration of oneimplementation and does not imply any limitations with regard to howdifferent embodiments may be implemented. Many modifications to thedepicted environments may be made based on design and implementationrequirements.

FIG. 4 is a block diagram 400 of internal and external components ofcomputers depicted in FIG. 1 in accordance with an illustrativeembodiment. It should be appreciated that FIG. 4 provides only anillustration of one implementation and does not imply any limitationswith regard to the environments in which different embodiments may beimplemented. Many modifications to the depicted environments may be madebased on design and implementation requirements.

Computer 102 (FIG. 1 ) and server computer 114 (FIG. 1 ) may includerespective sets of internal components 800A,B and external components900A,B illustrated in FIG. 5 . Each of the sets of internal components800 include one or more processors 820, one or more computer-readableRAMs 822 and one or more computer-readable ROMs 824 on one or more buses826, one or more operating systems 828, and one or morecomputer-readable tangible storage devices 830.

Processor 820 is implemented in hardware, firmware, or a combination ofhardware and software. Processor 820 is a central processing unit (CPU),a graphics processing unit (GPU), an accelerated processing unit (APU),a microprocessor, a microcontroller, a digital signal processor (DSP), afield-programmable gate array (FPGA), an application-specific integratedcircuit (ASIC), or another type of processing component. In someimplementations, processor 820 includes one or more processors capableof being programmed to perform a function. Bus 826 includes a componentthat permits communication among the internal components 800A,B.

The one or more operating systems 828, the software program 108 (FIG. 1) and the Speaker Identification Program 116 (FIG. 1 ) on servercomputer 114 (FIG. 1 ) are stored on one or more of the respectivecomputer-readable tangible storage devices 830 for execution by one ormore of the respective processors 820 via one or more of the respectiveRAMs 822 (which typically include cache memory). In the embodimentillustrated in FIG. 4 , each of the computer-readable tangible storagedevices 830 is a magnetic disk storage device of an internal hard drive.Alternatively, each of the computer-readable tangible storage devices830 is a semiconductor storage device such as ROM 824, EPROM, flashmemory, an optical disk, a magneto-optic disk, a solid state disk, acompact disc (CD), a digital versatile disc (DVD), a floppy disk, acartridge, a magnetic tape, and/or another type of non-transitorycomputer-readable tangible storage device that can store a computerprogram and digital information.

Each set of internal components 800A,B also includes a R/W drive orinterface 832 to read from and write to one or more portablecomputer-readable tangible storage devices 936 such as a CD-ROM, DVD,memory stick, magnetic tape, magnetic disk, optical disk orsemiconductor storage device. A software program, such as the softwareprogram 108 (FIG. 1 ) and the Speaker Identification Program 116 (FIG. 1) can be stored on one or more of the respective portablecomputer-readable tangible storage devices 936, read via the respectiveR/W drive or interface 832 and loaded into the respective hard drive830.

Each set of internal components 800A,B also includes network adapters orinterfaces 836 such as a TCP/IP adapter cards; wireless Wi-Fi interfacecards; or 3G, 4G, or 5G wireless interface cards or other wired orwireless communication links. The software program 108 (FIG. 1 ) and theSpeaker Identification Program 116 (FIG. 1 ) on the server computer 114(FIG. 1 ) can be downloaded to the computer 102 (FIG. 1 ) and servercomputer 114 from an external computer via a network (for example, theInternet, a local area network or other, wide area network) andrespective network adapters or interfaces 836. From the network adaptersor interfaces 836, the software program 108 and the SpeakerIdentification Program 116 on the server computer 114 are loaded intothe respective hard drive 830. The network may comprise copper wires,optical fibers, wireless transmission, routers, firewalls, switches,gateway computers and/or edge servers.

Each of the sets of external components 900A,B can include a computerdisplay monitor 920, a keyboard 930, and a computer mouse 934. Externalcomponents 900A,B can also include touch screens, virtual keyboards,touch pads, pointing devices, and other human interface devices. Each ofthe sets of internal components 800A,B also includes device drivers 840to interface to computer display monitor 920, keyboard 930 and computermouse 934. The device drivers 840, R/W drive or interface 832 andnetwork adapter or interface 836 comprise hardware and software (storedin storage device 830 and/or ROM 824).

It is understood in advance that although this disclosure includes adetailed description on cloud computing, implementation of the teachingsrecited herein are not limited to a cloud computing environment. Rather,some embodiments are capable of being implemented in conjunction withany other type of computing environment now known or later developed.

Cloud computing is a model of service delivery for enabling convenient,on-demand network access to a shared pool of configurable computingresources (e.g. networks, network bandwidth, servers, processing,memory, storage, applications, virtual machines, and services) that canbe rapidly provisioned and released with minimal management effort orinteraction with a provider of the service. This cloud model may includeat least five characteristics, at least three service models, and atleast four deployment models.

Characteristics are as follows:

On-demand self-service: a cloud consumer can unilaterally provisioncomputing capabilities, such as server time and network storage, asneeded automatically without requiring human interaction with theservice's provider.

Broad network access: capabilities are available over a network andaccessed through standard mechanisms that promote use by heterogeneousthin or thick client platforms (e.g., mobile phones, laptops, and PDAs).

Resource pooling: the provider's computing resources are pooled to servemultiple consumers using a multi-tenant model, with different physicaland virtual resources dynamically assigned and reassigned according todemand. There is a sense of location independence in that the consumergenerally has no control or knowledge over the exact location of theprovided resources but may be able to specify location at a higher levelof abstraction (e.g., country, state, or datacenter).

Rapid elasticity: capabilities can be rapidly and elasticallyprovisioned, in some cases automatically, to quickly scale out andrapidly released to quickly scale in. To the consumer, the capabilitiesavailable for provisioning often appear to be unlimited and can bepurchased in any quantity at any time.

Measured service: cloud systems automatically control and optimizeresource use by leveraging a metering capability at some level ofabstraction appropriate to the type of service (e.g., storage,processing, bandwidth, and active user accounts). Resource usage can bemonitored, controlled, and reported providing transparency for both theprovider and consumer of the utilized service.

Service Models are as follows:

Software as a Service (SaaS): the capability provided to the consumer isto use the provider's applications running on a cloud infrastructure.The applications are accessible from various client devices through athin client interface such as a web browser (e.g., web-based e-mail).The consumer does not manage or control the underlying cloudinfrastructure including network, servers, operating systems, storage,or even individual application capabilities, with the possible exceptionof limited user-specific application configuration settings.

Platform as a Service (PaaS): the capability provided to the consumer isto deploy onto the cloud infrastructure consumer-created or acquiredapplications created using programming languages and tools supported bythe provider. The consumer does not manage or control the underlyingcloud infrastructure including networks, servers, operating systems, orstorage, but has control over the deployed applications and possiblyapplication hosting environment configurations.

Infrastructure as a Service (laaS): the capability provided to theconsumer is to provision processing, storage, networks, and otherfundamental computing resources where the consumer is able to deploy andrun arbitrary software, which can include operating systems andapplications. The consumer does not manage or control the underlyingcloud infrastructure but has control over operating systems, storage,deployed applications, and possibly limited control of select networkingcomponents (e.g., host firewalls).

Deployment Models are as follows:

Private cloud: the cloud infrastructure is operated solely for anorganization. It may be managed by the organization or a third party andmay exist on-premises or off-premises.

Community cloud: the cloud infrastructure is shared by severalorganizations and supports a specific community that has shared concerns(e.g., mission, security requirements, policy, and complianceconsiderations). It may be managed by the organizations or a third partyand may exist on-premises or off-premises.

Public cloud: the cloud infrastructure is made available to the generalpublic or a large industry group and is owned by an organization sellingcloud services.

Hybrid cloud: the cloud infrastructure is a composition of two or moreclouds (private, community, or public) that remain unique entities butare bound together by standardized or proprietary technology thatenables data and application portability (e.g., cloud bursting forload-balancing between clouds).

A cloud computing environment is service oriented with a focus onstatelessness, low coupling, modularity, and semantic interoperability.At the heart of cloud computing is an infrastructure comprising anetwork of interconnected nodes.

Referring to FIG. 5 , illustrative cloud computing environment 500 isdepicted. As shown, cloud computing environment 500 comprises one ormore cloud computing nodes 10 with which local computing devices used bycloud consumers, such as, for example, personal digital assistant (PDA)or cellular telephone 54A, desktop computer 54B, laptop computer 54C,and/or automobile computer system 54N may communicate. Cloud computingnodes 10 may communicate with one another. They may be grouped (notshown) physically or virtually, in one or more networks, such asPrivate, Community, Public, or Hybrid clouds as described hereinabove,or a combination thereof. This allows cloud computing environment 500 tooffer infrastructure, platforms and/or software as services for which acloud consumer does not need to maintain resources on a local computingdevice. It is understood that the types of computing devices 54A-N shownin FIG. 5 are intended to be illustrative only and that cloud computingnodes 10 and cloud computing environment 500 can communicate with anytype of computerized device over any type of network and/or networkaddressable connection (e.g., using a web browser).

Referring to FIG. 6 , a set of functional abstraction layers 600provided by cloud computing environment 500 (FIG. 5 ) is shown. Itshould be understood in advance that the components, layers, andfunctions shown in FIG. 6 are intended to be illustrative only andembodiments are not limited thereto. As depicted, the following layersand corresponding functions are provided:

Hardware and software layer 60 includes hardware and softwarecomponents. Examples of hardware components include: mainframes 61; RISC(Reduced Instruction Set Computer) architecture based servers 62;servers 63; blade servers 64; storage devices 65; and networks andnetworking components 66. In some embodiments, software componentsinclude network application server software 67 and database software 68.

Virtualization layer 70 provides an abstraction layer from which thefollowing examples of virtual entities may be provided: virtual servers71; virtual storage 72; virtual networks 73, including virtual privatenetworks; virtual applications and operating systems 74; and virtualclients 75.

In one example, management layer 80 may provide the functions describedbelow. Resource provisioning 81 provides dynamic procurement ofcomputing resources and other resources that are utilized to performtasks within the cloud computing environment. Metering and Pricing 82provide cost tracking as resources are utilized within the cloudcomputing environment, and billing or invoicing for consumption of theseresources. In one example, these resources may comprise applicationsoftware licenses. Security provides identity verification for cloudconsumers and tasks, as well as protection for data and other resources.User portal 83 provides access to the cloud computing environment forconsumers and system administrators. Service level management 84provides cloud computing resource allocation and management such thatrequired service levels are met. Service Level Agreement (SLA) planningand fulfillment 85 provide pre-arrangement for, and procurement of,cloud computing resources for which a future requirement is anticipatedin accordance with an SLA.

Workloads layer 90 provides examples of functionality for which thecloud computing environment may be utilized. Examples of workloads andfunctions which may be provided from this layer include: mapping andnavigation 91; software development and lifecycle management 92; virtualclassroom education delivery 93; data analytics processing 94;transaction processing 95; and Speaker Identification 96. SpeakerIdentification 96 may identify a speaker in a text-based work.

Some embodiments may relate to a system, a method, and/or a computerreadable medium at any possible technical detail level of integration.The computer readable medium may include a computer-readablenon-transitory storage medium (or media) having computer readableprogram instructions thereon for causing a processor to carry outoperations.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program code/instructions for carrying out operationsmay be assembler instructions, instruction-set-architecture (ISA)instructions, machine instructions, machine dependent instructions,microcode, firmware instructions, state-setting data, configuration datafor integrated circuitry, or either source code or object code writtenin any combination of one or more programming languages, including anobject oriented programming language such as Smalltalk, C++, or thelike, and procedural programming languages, such as the “C” programminglanguage or similar programming languages. The computer readable programinstructions may execute entirely on the user's computer, partly on theuser's computer, as a stand-alone software package, partly on the user'scomputer and partly on a remote computer or entirely on the remotecomputer or server. In the latter scenario, the remote computer may beconnected to the user's computer through any type of network, includinga local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider). In some embodiments,electronic circuitry including, for example, programmable logiccircuitry, field-programmable gate arrays (FPGA), or programmable logicarrays (PLA) may execute the computer readable program instructions byutilizing state information of the computer readable programinstructions to personalize the electronic circuitry, in order toperform aspects or operations.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer readable media according to variousembodiments. In this regard, each block in the flowchart or blockdiagrams may represent a module, segment, or portion of instructions,which comprises one or more executable instructions for implementing thespecified logical function(s). The method, computer system, and computerreadable medium may include additional blocks, fewer blocks, differentblocks, or differently arranged blocks than those depicted in theFigures. In some alternative implementations, the functions noted in theblocks may occur out of the order noted in the Figures. For example, twoblocks shown in succession may, in fact, be executed concurrently orsubstantially concurrently, or the blocks may sometimes be executed inthe reverse order, depending upon the functionality involved. It willalso be noted that each block of the block diagrams and/or flowchartillustration, and combinations of blocks in the block diagrams and/orflowchart illustration, can be implemented by special purposehardware-based systems that perform the specified functions or acts orcarry out combinations of special purpose hardware and computerinstructions.

It will be apparent that systems and/or methods, described herein, maybe implemented in different forms of hardware, firmware, or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods were described herein without reference tospecific software code—it being understood that software and hardwaremay be designed to implement the systems and/or methods based on thedescription herein.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Furthermore,as used herein, the term “set” is intended to include one or more items(e.g., related items, unrelated items, a combination of related andunrelated items, etc.), and may be used interchangeably with “one ormore.” Where only one item is intended, the term “one” or similarlanguage is used. Also, as used herein, the terms “has,” “have,”“having,” or the like are intended to be open-ended terms. Further, thephrase “based on” is intended to mean “based, at least in part, on”unless explicitly stated otherwise.

The descriptions of the various aspects and embodiments have beenpresented for purposes of illustration, but are not intended to beexhaustive or limited to the embodiments disclosed. Even thoughcombinations of features are recited in the claims and/or disclosed inthe specification, these combinations are not intended to limit thedisclosure of possible implementations. In fact, many of these featuresmay be combined in ways not specifically recited in the claims and/ordisclosed in the specification. Although each dependent claim listedbelow may directly depend on only one claim, the disclosure of possibleimplementations includes each dependent claim in combination with everyother claim in the claim set. Many modifications and variations will beapparent to those of ordinary skill in the art without departing fromthe scope of the described embodiments. The terminology used herein waschosen to best explain the principles of the embodiments, the practicalapplication or technical improvement over technologies found in themarketplace, or to enable others of ordinary skill in the art tounderstand the embodiments disclosed herein.

What is claimed is:
 1. A method of identifying a speaker in a text-basedwork, executable by a processor, comprising: extracting labeled andunlabeled instances corresponding to one or more speakers; inferringpseudo-labels for the extracted unlabeled instances based on the labeledinstances; and labeling one or more of the unlabeled instances based onthe inferred pseudo-labels.
 2. The method of claim 1, further comprisingtraining a first model based on the labeled instances.
 3. The method ofclaim 2, further comprising training a second model based on theinferred pseudo-labels and the labeled instances.
 4. The method of claim3, further comprising replacing the first model with the second model.5. The method of claim 1, wherein the labeled and unlabeled instancescorrespond to a speaker or a quotation
 6. The method of claim 1, whereinthe labeled and unlabeled instances correspond to a class token, tokensin a first piece of text containing an utterance, a separator token, andtokens in a second piece of that covers the first piece of text.
 7. Themethod of claim 6, wherein two vectors correspond to estimatedprobabilities of each of the tokens being a starting token or an endingtoken of an answer span that appears in the second piece of text.
 8. Acomputer system for identifying a speaker in a text-based work, thecomputer system comprising: one or more computer-readable non-transitorystorage media configured to store computer program code; and one or morecomputer processors configured to access said computer program code andoperate as instructed by said computer program code, said computerprogram code including: extracting code configured to cause the one ormore computer processors to extract labeled and unlabeled instancescorresponding to one or more speakers; inferring code configured tocause the one or more computer processors to infer pseudo-labels for theextracted unlabeled instances based on the labeled instances; andlabeling code configured to cause the one or more computer processors tolabel one or more of the unlabeled instances based on the inferredpseudo-labels.
 9. The computer system of claim 8, further comprisingtraining code configured to cause the one or more computer processors totrain a first model based on the labeled instances.
 10. The computersystem of claim 9, further comprising training code configured to causethe one or more computer processors to train a second model based on theinferred pseudo-labels and the labeled instances.
 11. The computersystem of claim 10, further comprising replacing code configured tocause the one or more computer processors to replace the first modelwith the second model.
 12. The computer system of claim 8, wherein thelabeled and unlabeled instances correspond to a speaker or a quotation13. The computer system of claim 8, wherein the labeled and unlabeledinstances correspond to a class token, tokens in a first piece of textcontaining an utterance, a separator token, and tokens in a second pieceof that covers the first piece of text.
 14. The computer system of claim13, wherein two vectors correspond to estimated probabilities of each ofthe tokens being a starting token or an ending token of an answer spanthat appears in the second piece of text.
 15. A non-transitory computerreadable medium having stored thereon a computer program for identifyinga speaker in a text-based work, the computer program configured to causeone or more computer processors to: extract labeled and unlabeledinstances corresponding to one or more speakers; infer pseudo-labels forthe extracted unlabeled instances based on the labeled instances; andlabel one or more of the unlabeled instances based on the inferredpseudo-labels.
 16. The computer readable medium of claim 15, wherein thecomputer program is further configured to cause one or more computerprocessors to train a first model based on the labeled instances. 17.The computer readable medium of claim 16, wherein the computer programis further configured to cause one or more computer processors to traina second model based on the inferred pseudo-labels and the labeledinstances.
 18. The computer readable medium of claim 17, wherein thecomputer program is further configured to cause one or more computerprocessors to replace the first model with the second model.
 19. Thecomputer readable medium of claim 15, wherein the labeled and unlabeledinstances correspond to a speaker or a quotation
 20. The computerreadable medium of claim 15, wherein the labeled and unlabeled instancescorrespond to a class token, tokens in a first piece of text containingan utterance, a separator token, and tokens in a second piece of thatcovers the first piece of text.